Fast simulation of electromagnetic particle showers in high granularity calorimeters
24TH INTERNATIONAL CONFERENCE ON COMPUTING IN HIGH ENERGY AND NUCLEAR PHYSICS (CHEP 2019)(2020)
摘要
The future need of simulated events by the LHC experiments and their High Luminosity upgrades, is expected to increase by one or two orders of magnitude. As a consequence, research on new fast simulation solutions, including deep Generative Models, is very active and initial results look promising. We have previously reported on a prototype that we have developed, based on 3 dimensional convolutional Generative Adversarial Network, to simulate particle showers in high-granularity calorimeters. In this contribution we present improved results on a more realistic simulation. Detailed validation studies show very good agreement with Monte Carlo simulation. In particular, we show how increasing the network representational power, introducing physics-based constraints and using a transfer-learning approach for training improve the level of agreement over a large energy range.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要